Diagnostic value of blood lipids combined with blood routine parameters for pneumoconiosis and the construction of nomogram prediction model
10.3969/j.issn.1673-4130.2025.08.013
- VernacularTitle:构建基于血脂联合血常规相关参数的列线图诊断模型及其对尘肺病的诊断价值分析
- Author:
Qu ZHOU
1
;
Wei WANG
;
Zimeng WANG
;
Longchun MAO
;
Juan HU
;
Yuanyuan LI
;
Junli YU
;
Shangcheng XU
;
Wenbing LIU
Author Information
1. 重庆医药高等专科学校附属第一医院健康管理中心,重庆 400060
- Keywords:
pneumoconiosis;
blood lipids;
blood routine;
nomogram prediction model
- From:
International Journal of Laboratory Medicine
2025;46(8):965-970,975
- CountryChina
- Language:Chinese
-
Abstract:
Objective To analyze the situation of blood lipid and blood routine parameters in patients with pneumoconiosis,and construct a column chart diagnostic model to explore their diagnostic value for pneumo-coniosis.Methods A total of 456 patients with pneumoconiosis admitted to the First Affiliated Hospital of Chongqing Medical and Pharmaceutical College from January 2022 to January 2024 were selected as the pneu-moconiosis group,while 462 healthy subjects exposed to dust during the same period were chosen as the con-trol group.Serum lipids and blood routine parameters related to pneumoconiosis were measured and compared between two groups.Univariate and multivariate Logistic regression analyzes were conducted to examine ser-um lipids and blood routine parameters associated with pneumoconiosis.A risk prediction model was construc-ted using logistic regression in machine learning,and the diagnostic efficacy of the column chart diagnostic model was evaluated by calculating the C-index through receiver operating characteristic(ROC)curve and plotting the model calibration curve based on Hosmer Lemeshow goodness of fit.Decision curve analysis(DCA)was used to assess the clinical practicality of the column chart diagnostic model.Results The levels of serum high-density ester protein cholesterol(HDL-C),cholesterol(TC),red blood cell(RBC),hematocrit(HCT),hemoglobin concentration(HGB),lymphocyte number(LYM),and lymphocyte percentage(LYM％)in the pneumoconiosis group were lower than those in the control group(P＜0.05).The levels of neutrophil-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR),and systemic immune inflammation index(SII)were higher than those in the control group(P＜0.05).Multivariate Logistic regression analysis showed that HDL-C,LYM％,PLR,and SII were independent influencing factors for pneumoconiosis(P＜0.05).A column chart diagnostic model for the occurrence of pneumoconiosis was constructed using HDL-C,TC,LYM％,PLR,and SII as diagnostic factors.The ROC curve C-index of the diagnostic model was 0.84(95％CI:0.81-0.86),with sensitivity for diagnosing pneumoconiosis of 75.29％,specificity of 77.51％,posi-tive predictive value of 83.25％,and negative predictive value of 67.88％.Internal validation was conducted on the constructed column chart diagnostic model,with a validation set ROC curve C-index of 0.84(95％CI:0.80-0.87),sensitivity of 80.91％,specificity of 72.62％,positive diagnostic value of 79.46％,and negative diagnostic value of 74.39％.The calibration positive curve slope of the diagnostic model was close to 1,and in the fit test P＞0.05.DCA analysis showed that the diagnostic model had clinical practical value for risk diag-nosis of pneumoconiosis.Conclusion HDL-C,TC,LYM％,PLR and SII are independent influencing factors for pneumoconiosis.A column chart diagnostic model for the occurrence of pneumoconiosis is successfully constructed based on machine learning principles,and it has been verified to have high diagnostic efficiency.
